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Normalizing flows are a class of deep generative models that provide a promising route to sample lattice field theories more efficiently than conventional Monte Carlo simulations.
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M. Campisi and J. Goold, Thermodynamics of quantum information scrambling , Phys. Rev. E 95
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N. Yunger Halpern, Jarzynski-like equality for the out-of-time-ordered correlator , Phys. Rev. A 95
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A. Radovic, M. Williams, D. Rousseau, M. Kagan, D. Bonacorsi, A. Himmel et al., Machine learning at the energy and intensity frontiers of particle physics , Nature 560
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S.-H. Li and L. Wang, Neural Network Renormalization Group , Phys. Rev. Lett. 121
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2019
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D. Bachtis, G. Aarts and B. Lucini, Quantum field-theoretic machine learning , Phys. Rev. D 103
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A. Kosowsky, M. S. Turner and R. Watkins, Gravitational waves from first order cosmological phase transitions , Phys. Rev. Lett. 69
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